Crossref Citations
This article has been cited by the following publications. This list is generated based on data provided by
Crossref.
Kock, Anders Bredahl
and
Callot, Laurent
2012.
Oracle Inequalities for High Dimensional Vector Autoregressions.
SSRN Electronic Journal,
Qian, Junhui
and
Su, Liangjun
2014.
Shrinkage Estimation of Common Breaks in Panel Data Models via Adaptive Group Fused Lasso.
SSRN Electronic Journal,
Chen, Jia
and
Gao, Jiti
2014.
Semiparametric Model Selection in Panel Data Models with Deterministic Trending and Cross-Sectional Dependence.
SSRN Electronic Journal,
Lu, Xun
and
Su, Liangjun
2016.
Shrinkage estimation of dynamic panel data models with interactive fixed effects.
Journal of Econometrics,
Vol. 190,
Issue. 1,
p.
148.
Qian, Junhui
and
Su, Liangjun
2016.
Shrinkage estimation of common breaks in panel data models via adaptive group fused Lasso.
Journal of Econometrics,
Vol. 191,
Issue. 1,
p.
86.
Qian, Junhui
and
Su, Liangjun
2016.
SHRINKAGE ESTIMATION OF REGRESSION MODELS WITH MULTIPLE STRUCTURAL CHANGES.
Econometric Theory,
Vol. 32,
Issue. 6,
p.
1376.
Kock, Anders Bredahl
2016.
Oracle inequalities, variable selection and uniform inference in high-dimensional correlated random effects panel data models.
Journal of Econometrics,
Vol. 195,
Issue. 1,
p.
71.
Caner, Mehmet
and
Kock, Anders Bredahl
2016.
Oracle Inequalities for Convex Loss Functions with Nonlinear Targets.
Econometric Reviews,
Vol. 35,
Issue. 8-10,
p.
1377.
Feng, Guohua
Gao, Jiti
Peng, Bin
and
Zhang, Xiaohui
2017.
A varying-coefficient panel data model with fixed effects: Theory and an application to US commercial banks.
Journal of Econometrics,
Vol. 196,
Issue. 1,
p.
68.
Kock, Anders Bredahl
and
Tang, Haihan
2019.
UNIFORM INFERENCE IN HIGH-DIMENSIONAL DYNAMIC PANEL DATA MODELS WITH APPROXIMATELY SPARSE FIXED EFFECTS.
Econometric Theory,
Vol. 35,
Issue. 2,
p.
295.
Wu, Xianyi
and
Zhou, Xian
2019.
On Hodges’ superefficiency and merits of oracle property in model selection.
Annals of the Institute of Statistical Mathematics,
Vol. 71,
Issue. 5,
p.
1093.
Uematsu, Yoshimasa
and
Tanaka, Shinya
2019.
High-dimensional macroeconomic forecasting and variable selection via penalized regression.
The Econometrics Journal,
Vol. 22,
Issue. 1,
p.
34.
Wu, Yunan
and
Wang, Lan
2020.
A Survey of Tuning Parameter Selection for High-Dimensional Regression.
Annual Review of Statistics and Its Application,
Vol. 7,
Issue. 1,
p.
209.
Harding, Matthew C.
and
Lamarche, Carlos
2021.
Small Steps with Big Data: Using Machine Learning in Energy and Environmental Economics.
Annual Review of Resource Economics,
Vol. 13,
Issue. 1,
p.
469.
Pretis, Felix
and
Schwarz, Moritz
2022.
Discovering What Mattered: Answering Reverse Causal Questions by Detecting Unknown Treatment Assignment and Timing as Breaks in Panel Models.
SSRN Electronic Journal,
Babii, Andrii
Ball, Ryan T.
Ghysels, Eric
and
Striaukas, Jonas
2023.
Machine learning panel data regressions with heavy-tailed dependent data: Theory and application.
Journal of Econometrics,
Vol. 237,
Issue. 2,
p.
105315.
Lamarche, Carlos
and
Parker, Thomas
2023.
Wild bootstrap inference for penalized quantile regression for longitudinal data.
Journal of Econometrics,
Vol. 235,
Issue. 2,
p.
1799.
Chiang, Harold D.
Rodrigue, Joel
and
Sasaki, Yuya
2023.
POST-SELECTION INFERENCE IN THREE-DIMENSIONAL PANEL DATA.
Econometric Theory,
Vol. 39,
Issue. 3,
p.
623.
Mei, Xiaoling
Peng, Bin
and
Zhu, Huanjun
2023.
Variable selection in heterogeneous panel data models with cross‐sectional dependence.
Australian & New Zealand Journal of Statistics,
Vol. 65,
Issue. 1,
p.
14.